25 citations · 35 across the 23 of their papers we have counts for
9 papers · 1 filter
EviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause Analysis
Yuhao Wang, Zhen Qin, Xingliang Wang +3
Root-cause analysis (RCA) is a critical yet labor-intensive task for maintaining modern microservice systems, making it an attractive target for large language models (LLMs). Recen…
Can Vision-Language Models Handle Long-Context Code? An Empirical Study on Visual Compression
Jianping Zhong, Guochang Li, Chen Zhi +6
Large Language Models (LLMs) struggle with long-context code due to window limitations. Existing textual code compression methods mitigate this via selective filtering but often di…
Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion
Baoyi Wang, Xingliang Wang, Guochang Li +6
Repository-level code completion remains challenging for large language models (LLMs) due to cross-file dependencies and limited context windows. Prior work addresses this challeng…
Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding
Xinkui Zhao, Rongkai Liu, Yifan Zhang +6
As code completion task from function-level to repository-level, leveraging contextual information from large-scale codebases becomes a core challenge. However, existing retrieval-…
Revisiting Vulnerability Patch Localization: An Empirical Study and LLM-Based Solution
Haoran Xu, Chen Zhi, Junxiao Han +3
Open-source software vulnerability patch detection is a critical component for maintaining software security and ensuring software supply chain integrity. Traditional manual detect…
GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion
Xingliang Wang, Baoyi Wang, Chen Zhi +4
LLMs excel in localized code completion but struggle with repository-level tasks due to limited context windows and complex semantic and structural dependencies across codebases. W…
LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
Junxiao Han, Yarong Wang, Xiaodong Gu +5
In this paper, we propose LibRec, a novel framework that integrates the capabilities of LLMs with retrieval-augmented generation(RAG) techniques to automate the recommendation of a…
Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair
Guochang Li, Chen Zhi, Jialiang Chen +2
Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models…
ChatUniTest: A Framework for LLM-Based Test Generation
Yinghao Chen, Zehao Hu, Chen Zhi +3
Unit testing is an essential yet frequently arduous task. Various automated unit test generation tools have been introduced to mitigate this challenge. Notably, methods based on la…